I write a function to get the Validation accuracy

def evaluation(loader, model, device):
    model.eval()
    model.to(device)
    correct = 0
    total = len(loader.dataset)
    for data in loader:
        with torch.no_grad():
            inputs, labels = data[0].to(device), data[1].to(device)
            pred = model(inputs)
            pred = pred.argmax(dim=1)
        correct += pred.eq(labels).sum().item()
 
    acc = correct / total
    return acc

but when i testing it I found that the for loop is been skipped. Why?
My loader and dataset are in this picture
enter image description here

the dataloader code:

   data_list = list(range(0, len(dataset)))
    val_list = random.sample(data_list, int(len(dataset) * val_split))
    trainset = [dataset[i] for i in data_list if i not in val_list]
    valset = [dataset[i] for i in data_list if i in val_list]

    # Weighed Sampling
    if weighted_sampling:
        label_count = Counter([int(data[1]) for data in dataset])
        weights = [100 / label_count[int(data[1])] for data in trainset]
        sampler = WeightedRandomSampler(weights, num_samples=len(trainset), replacement=True)
        train_loader = DataLoader(trainset, batch_size=batch_size, sampler=sampler, drop_last=True)
    else:
        train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=False, drop_last=True)

    val_loader = DataLoader(valset, batch_size=batch_size, shuffle=True, drop_last=True)

I have tried to check my dataset and dataloader, but I can’t found someting wrong.
I am expecting for the reason, thanks！